A collaborative filtering may refer to a technique that identifies information a user may be interested in. The collaborative filtering may first define preferences of a group of users who have used a product in order to set up a recommendation system (e.g., which may enhance a buying experience of the user through recommending the product when the user is likely to enjoy the product).
The collaborative filtering may be based on a rating of the user when the product is categorized according to a genre of the product (e.g., which may be relevant in cases of books, products, songs, etc.). The rating based on the genre of the product may tell a company (e.g., employing a genre-based rating system) about a taste of the user and/or help the company to highlight the product which the user most likely enjoy. However, there may not be enough information in the rating based on the genre, to achieve desired results of a successful product recommendation because a domain of the rating based on the genre may not reach a desired range of the successful product recommendation. The rating based on the genre may be too general where thousands of products may fit into a particular genre.
Alternatively, the company may allow the group of users to create tags about the product. However, there may be no structure to link the tags (e.g., which may be random) together. For instance, two users may like a product for a similar reason, but information that the two users like the product for a particular reason may not be derived if the two users were to use different tags to describe the product. In sum, the collaborative filtering based on the genre and/or the tags may be too general and/or arbitrary to be effective in recommending the product. This may hinder the company from getting more business from the user who may be discouraged from buying more products from the company when the user finds the products recommended by the collaborative filtering may not be that relevant to interests and/or needs of the user.